{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e685b25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import csv\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "# 网页地址\n",
    "url = \"https://www.ukpass.org/ranking/index-2-75-0.html\"\n",
    "# 获取网页内容\n",
    "response = requests.get(url)\n",
    "html_content = response.content\n",
    "# 解析网页内容\n",
    "soup = BeautifulSoup(html_content, 'html.parser')\n",
    "table = soup.find('table')\n",
    "# 获取表格内容，并写入csv和excel文件\n",
    "new_header = ['2024排名', '学校名称', '总分', 'H指数', '学术声誉', '雇主声誉']\n",
    "with open('2024世界大学艺术设计专业排名.csv', mode='w', newline='',encoding='utf-8-sig') as file:\n",
    "    writer = csv.writer(file)\n",
    "    writer.writerow(new_header)\n",
    "    for row in table.find_all('tr')[1:]:\n",
    "        row_data = []\n",
    "        cells = row.find_all(['th', 'td'])[:6]  # 只保留前六列数据\n",
    "        if cells:\n",
    "            for cell in cells:\n",
    "                row_data.append(cell.text.strip())\n",
    "            writer.writerow(row_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a885cdd9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   2024排名  100 non-null    int64  \n",
      " 1   学校名称    100 non-null    object \n",
      " 2   总分      50 non-null     float64\n",
      " 3   H指数     0 non-null      float64\n",
      " 4   学术声誉    100 non-null    float64\n",
      " 5   雇主声誉    99 non-null     float64\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 4.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(   2024排名                                               学校名称    总分   学术声誉  \\\n",
       " 0       1                         皇家艺术学院Royal Academy of Art  98.5  100.0   \n",
       " 1       2                伦敦艺术大学University of the Arts London  93.7   93.8   \n",
       " 2       3                    新学院大学The New College University  92.3   93.7   \n",
       " 3       4  罗德岛设计学院（RISD）Rhode Island School of Design (RISD)  91.7   93.3   \n",
       " 4       5                                          麻省理工学院Mit  85.3   83.7   \n",
       " \n",
       "     雇主声誉       国家  \n",
       " 0   85.0  Unknown  \n",
       " 1   93.1  Unknown  \n",
       " 2   79.7  Unknown  \n",
       " 3   77.6  Unknown  \n",
       " 4  100.0  Unknown  ,\n",
       " Empty DataFrame\n",
       " Columns: [2024排名, 学校名称, 总分, 学术声誉, 雇主声誉]\n",
       " Index: [],\n",
       " Empty DataFrame\n",
       " Columns: [2024排名, 学校名称, 总分, 学术声誉, 雇主声誉]\n",
       " Index: [],\n",
       " Empty DataFrame\n",
       " Columns: [2024排名, 学校名称, 总分, 学术声誉, 雇主声誉]\n",
       " Index: [])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "file_path = '2024世界大学艺术设计专业排名.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "data.head(), data.info(), data.describe()\n",
    "\n",
    "# 第一步：处理缺失值\n",
    "# 删除“H指数”列，因为它只包含NaN值\n",
    "data_cleaned = data.drop(columns=['H指数'])\n",
    "\n",
    "# 用各自的平均值填充“总分”和“雇主声誉”的缺失值\n",
    "data_cleaned['总分'] = data_cleaned['总分'].fillna(data_cleaned['总分'].mean())\n",
    "data_cleaned['雇主声誉'] = data_cleaned['雇主声誉'].fillna(data_cleaned['雇主声誉'].mean())\n",
    "\n",
    "# 第二步：处理错误数据\n",
    "# 假设分数必须在0到100之间，检查是否有任何此类错误。\n",
    "erroneous_total_scores = data_cleaned[(data_cleaned['总分'] < 0) | (data_cleaned['总分'] > 100)]\n",
    "erroneous_academic_scores = data_cleaned[(data_cleaned['学术声誉'] < 0) | (data_cleaned['学术声誉'] > 100)]\n",
    "erroneous_employer_scores = data_cleaned[(data_cleaned['雇主声誉'] < 0) | (data_cleaned['雇主声誉'] > 100)]\n",
    "\n",
    "# 第三步：特征提取\n",
    "# 如果学校名称包含常见国家名称或缩写，则提取国家名称\n",
    "import re\n",
    "data_cleaned['国家'] = data_cleaned['学校名称'].apply(lambda x: re.findall(r'(?i)(china|uk|usa|canada|germany)', x))\n",
    "data_cleaned['国家'] = data_cleaned['国家'].apply(lambda x: x[0].upper() if x else 'Unknown')\n",
    "\n",
    "data_cleaned.head(), erroneous_total_scores, erroneous_academic_scores, erroneous_employer_scores\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed4b3289",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# 数据分布分析与可视化：总分的分布\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data_cleaned['总分'], kde=True, color='blue')\n",
    "plt.title('总分分布图')\n",
    "plt.xlabel('总分')\n",
    "plt.ylabel('频数')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c481f733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分类数据分析与可视化：按国家分析学术声誉的平均值\n",
    "country_academic_reputation = data_cleaned.groupby('国家')['学术声誉'].mean().reset_index()\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(x='国家', y='学术声誉', data=country_academic_reputation)\n",
    "plt.title('各国学术声誉平均分比较')\n",
    "plt.xlabel('国家')\n",
    "plt.ylabel('学术声誉平均分')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "71e94c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 关联分析与可视化：相关系数热图\n",
    "plt.figure(figsize=(10, 8))\n",
    "corr_matrix = data_cleaned[['总分', '学术声誉', '雇主声誉']].corr()\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('变量间相关系数热图')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bbe4857",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
